Machine Learning Basics - SSS 2 plan

LESSON PLAN

MACHINE LEARNING BASICS

Subject	Digital Technologies
Class	SSS 2
Term	Second Term
Week	10
Topic	Machine Learning Basics
Duration	80 Minutes (Double Period)
Date	________________

Instructional Objectives

By the end of the lesson, students should be able to:

1. Define machine learning.
2. Explain the relationship between Artificial Intelligence and Machine Learning.
3. Identify simple applications of machine learning.
4. Describe how machine-learning systems learn from data.
5. Outline the basic stages involved in developing a machine-learning model.
6. State the benefits, limitations and responsible practices associated with machine learning.

Previous Knowledge

Students have previously learned about Artificial Intelligence and Big Data and Analytics. They are familiar with the use of digital systems to process information and perform tasks.

Instructional Materials

• Computer or laptop, where available.
• Whiteboard and markers.
• Charts showing the relationship between AI and machine learning.
• Examples of spam messages and normal emails.
• Pictures or demonstrations of recommendation systems and image recognition.
• Projector or smartphone, where available.

Teaching Methods

Explanation, questioning, discussion, demonstration, brainstorming and guided classroom activities.

Presentation Steps

Time	Teacher's Activities	Students' Activities
10 min	Step 1: Introduction Revises the previous lesson on Artificial Intelligence. Asks students how email services identify spam and how music applications recommend songs. Introduces machine learning.	Respond to questions, share examples of digital systems they use and identify tasks computers can perform.
10 min	Step 2: Meaning of Machine Learning Defines machine learning as an approach that uses data to learn patterns for predictions or decisions. Explains the difference between learning from examples and following only fixed instructions.	Listen, take notes and explain the meaning of machine learning in their own words.
10 min	Step 3: Relationship Between AI and ML Explains that AI is the broader field and machine learning is one approach used to develop AI systems. Gives examples of rule-based systems and systems that learn from data.	Discuss the relationship between AI and ML and give examples of each.
15 min	Step 4: How Systems Learn from Data Explains data collection, preparation, training, testing, prediction and monitoring. Introduces supervised, unsupervised and reinforcement learning using simple examples.	Arrange the stages in the correct order, identify the types of learning and participate in guided discussion.
10 min	Step 5: Applications of Machine Learning Discusses spam filtering, predictive text, recommendation systems, speech recognition, image classification, fraud detection and prediction.	Identify familiar applications and explain how each may use data to make predictions or recommendations.
10 min	Step 6: Class Activity and Responsible Use Guides students to examine sample messages labelled as spam or not spam. Discusses how patterns may be identified and highlights errors, biased data and privacy risks.	Classify sample messages, identify possible patterns and state precautions for using machine-learning systems responsibly.
5 min	Step 7: Evaluation Asks oral questions to assess students' understanding of the meaning, relationship, applications and learning process of machine learning.	Answer questions individually and give examples of machine-learning applications.
5 min	Step 8: Conclusion and Assignment Summarises the main points, corrects misconceptions and gives the assignment.	Summarise what they have learned and record the assignment.
Total: 80 Minutes

Evaluation

Ask students the following questions:

1. What is machine learning?
2. Explain the relationship between Artificial Intelligence and Machine Learning.
3. Mention four applications of machine learning.
4. State three basic types of machine learning.
5. List four stages involved in developing or using a machine-learning model.
6. Mention three limitations or risks of machine learning.

Assignment

1. Define machine learning and explain its relationship with Artificial Intelligence.
2. Describe how an email spam filter may learn from examples.
3. List five applications of machine learning.
4. Differentiate between supervised learning and unsupervised learning.
5. State four precautions that should be taken when using machine-learning systems.

Reference

CMPNOTE Digital Technologies Lesson Note: Machine Learning Basics – SSS 2, Second Term, Week 10.